ArticleFrontiers in neuroinformatics2022
Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian.
Article in Frontiers in neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Computational Model of the Respiratory CPG for the Artificial Control of Breathing.Bioengineering (Basel, Switzerland) · 2025Article
- Bridging neuromorphic computing and deep learning for next-generation neural data interpretation.Frontiers in computational neuroscience · 2025Article
- The road toward a physiological control of artificial respiration: the role of bio-inspired neuronal networks.Frontiers in neuroscience · 2025Article
- Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing.Nature communications · 2024Article
- From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?Brain sciences · 2023Review
- Introducing the Dendrify framework for incorporating dendrites to spiking neural networks.Nature communications · 2023Article
- Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi.Frontiers in neuroinformatics · 2022Article
- Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi.Frontiers in neuroscience · 2022Article
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Authors and funding
4 authors.
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Abstract
Developing intelligent neuromorphic solutions remains a challenging endeavor. It requires a solid conceptual understanding of the hardware's fundamental building blocks. Beyond this, accessible and user-friendly prototyping is crucial to speed up the design pipeline. We developed an open source Loihi emulator based on the neural network simulator Brian that can easily be incorporated into existing simulation workflows. We demonstrate errorless Loihi emulation in software for a single neuron and for a recurrently connected spiking neural network. On-chip learning is also reviewed and implemented, with reasonable discrepancy due to stochastic rounding. This work provides a coherent presentation of Loihi's computational unit and introduces a new, easy-to-use Loihi prototyping package with the aim to help streamline conceptualization and deployment of new algorithms.
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Registered trials
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